A novel approach to segmentation and measurement of medical image using level set methods |
| |
| Institution: | 1. Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan;2. Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan;3. Division of Radiology, and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo;4. Philips Electronics Japan, Tokyo, Japan;5. Medical Imaging Laboratory, Graduate School of Information Sciences, Hiroshima City University;1. Department of Radiology, Weill Cornell Medical College, New York, NY, United States;2. Department of Neurology, Weill Cornell Medical College, New York, NY, United States;1. Mayo Graduate School, Biomedical Engineering and Physiology Track, Mayo Clinic, Rochester, Minnesota, USA;2. Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA;3. Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota, USA;1. Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women''s Medical University, Tokyo, Japan;2. Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan;3. Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan;4. Cardiovascular Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan;5. Department of Marketing Division, Philips Healthcare Japan, Tokyo, Japan |
| |
| Abstract: | The study proposes a novel approach for segmentation and visualization plus value-added surface area and volume measurements for brain medical image analysis. The proposed method contains edge detection and Bayesian based level set segmentation, surface and volume rendering, and surface area and volume measurements for 3D objects of interest (i.e., brain tumor, brain tissue, or whole brain).Two extensions based on edge detection and Bayesian level set are first used to segment 3D objects. Ray casting and a modified marching cubes algorithm are then adopted to facilitate volume and surface visualization of medical-image dataset. To provide physicians with more useful information for diagnosis, the surface area and volume of an examined 3D object are calculated by the techniques of linear algebra and surface integration. Experiment results are finally reported in terms of 3D object extraction, surface and volume rendering, and surface area and volume measurements for medical image analysis. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
